Enhancing Deep Learning Performance of Massive MIMO CSI Feedback

TL;DR

This paper introduces a jigsaw puzzle-inspired training strategy to improve deep learning-based CSI feedback in Massive MIMO systems, significantly boosting accuracy by maximizing mutual information between original and compressed data.

Contribution

It proposes a novel training method (JPTS) that enhances existing deep learning models for CSI feedback by leveraging data characteristics, leading to improved performance.

Findings

Boosts accuracy by 12.07% indoors

Increases accuracy by 7.01% outdoors

Compatible with existing deep learning frameworks

Abstract

CSI feedback is an important problem of Massive multiple-input multiple-output (MIMO) technology because the feedback overhead is proportional to the number of sub-channels and the number of antennas, both of which scale with the size of the Massive MIMO system. Deep learning-based CSI feedback methods have been widely adopted recently owing to their superior performance. Despite the success, current approaches have not fully exploited the relationship between the characteristics of CSI data and the deep learning framework. In this paper, we propose a jigsaw puzzles aided training strategy (JPTS) to enhance the deep learning-based Massive MIMO CSI feedback approaches by maximizing mutual information between the original CSI and the compressed CSI. We apply JPTS on top of existing state-of-the-art methods. Experimental results show that by adopting this training strategy, the accuracy can be boosted by 12.07% and 7.01% on average in indoor and outdoor environments, respectively. The proposed method is ready to adopt to existing deep learning frameworks of Massive MIMO CSI feedback. Codes of JPTS are available on GitHub for use.